引用是滞后的：从论文所相信的内容中解读认识论不稳定性，早于引用图追上数年

中文摘要 AI 辅助

科学中的范式转变在研究人员的主张和争论中可见，早于它们在引用图中显现。我们探究一个廉价的内容层面的认识论不稳定性信号，该信号源自论文摘要中陈述的建模信念分布的变化，能否比基于引用的主导性颠覆指数（CD5）更早地预测范式转变。在NLP中循环网络被Transformer取代的案例中，一个预注册的内容信号在2016年第一季度跨过其检测阈值，而实时CD5监测器直到2022年第二季度才能观察到2017年的突破，因为CD5需要五年的前向引用窗口：这带来了约25个季度的领先。平坦的引用基线并非某一指数的伪影，因为我们的CD5、一个参考归一化变体以及一个权威的预计算指数在转变期间都接近零。这种领先也不是更快的代理：在相同延迟下，该信号击败了测试的内容竞争对手，包括一个从文本学习的CD模型和一个嵌入颠覆度量，并且分解看到了关键词无法看到的东西（关键词盲AUC约为0.9，在人工标签上复现）。对CD5的领先是可观测性领先。哪个信号携带领先取决于转变：在NLP中是信念采纳，在视觉中是争论和适用性压力。相对于突破本身，该信号在NLP中领先五个季度，在计算机视觉中则是同时的。

英文摘要

Paradigm shifts in science are visible in what researchers assert and contest before they are visible in the citation graph. We ask whether a cheap, content-level signal of epistemic instability, derived from the changing distribution of stated modelling beliefs in paper abstracts, can anticipate a paradigm shift earlier than the dominant citation-based disruption index (CD5). On the displacement of recurrent networks by Transformers in NLP, a pre-registered content signal crosses its detection threshold in 2016-Q1, whereas a real-time CD5 monitor cannot even observe the 2017 breakthrough until 2022-Q2, since CD5 needs a five-year forward-citation window: a lead of about 25 quarters. The flat citation baseline is not an artifact of one index, as our CD5, a reference-normalised variant, and an authoritative precomputed index all sit near zero across the shift. The lead is also not a faster proxy: at equal latency the signal beats the content competitors tested, including a learned CD-from-text model and an embedding disruption measure, and the decomposition sees what a keyword cannot (keyword-blind AUC of about 0.9, reproduced on human labels). The lead over CD5 is an observability lead. Which signal carries it depends on the shift: belief adoption in NLP, contestation and applicability stress in vision. Measured against the breakthroughs themselves, the signal leads by five quarters in NLP and is contemporaneous in computer vision.

发表机构

• Constructor University（康斯特鲁克托尔大学）
• National University of Singapore（新加坡国立大学）
• Constructor Labs（康斯特鲁克托尔实验室）

机构由 AI 辅助整理，请以论文原文为准。